High-Speed Train Seismic Acquisition for Low-Impact Subterranean Imaging
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Solution Overview
Problem
Seismic surveys using controlled sources like dynamite or air guns are expensive and environmentally disruptive, and existing methods lack efficient alternatives for characterizing subterranean features.
Innovation Solution
Utilizing high-speed trains to generate seismic waves along train tracks, processing raw seismic datasets by removing direct waves, deconvolving seismic wavelets, correcting for normal moveout, stacking traces, and filtering artifacts to combine processed datasets for clear subterranean feature identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If controlled seismic sources (dynamite, air gun) are used to generate seismic waves, then seismic data can be acquired, but the cost increases and environmental disruption increases
Solution Approach 1:
The patent replaces expensive, environmentally harmful controlled seismic sources (dynamite, air guns) with a relatively inexpensive and environmentally benign high-speed train. The train serves as a disposable-like source that can be easily deployed and removed without causing significant environmental disruption, while still being capable of generating the necessary seismic waves for data acquisition.
Solution Approach 2:
The patent substitutes the traditional mechanical explosive or pneumatic seismic sources with a train-based mechanical system. The train's movement along the track generates seismic waves through its interaction with the ground and track structure, replacing the need for explosives or air guns while maintaining the ability to generate usable seismic data.
2Object-generated harmful factors
If high-speed train is used to generate seismic waves, then cost and environmental disruption are reduced, but data processing complexity increases
Solution Approach 1:
The patent divides the seismic data acquisition process into multiple segments by using multiple trains traveling at different speeds. Each train provides a separate dataset that can be processed independently, and the results are later combined. This segmentation allows for more manageable processing of individual datasets while reducing the overall complexity compared to using a single complex source system.
Solution Approach 2:
The patent utilizes varying train speeds as a parameter to generate different seismic datasets. By changing the speed parameter across multiple acquisitions, the system creates diverse data that can be processed and combined to improve the overall seismic image. This parameter variation approach transforms the complexity into a useful dimension for data enhancement.
3Measurement precision
If multiple raw seismic datasets from different train speeds are processed and combined, then subterranean feature identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing actions on each individual train dataset before combining them. By pre-processing each dataset to remove artifacts and enhance signals specific to that train's acquisition conditions, the system prepares the data for more efficient combination. This preliminary action reduces the computational burden during the final stacking process while maintaining high accuracy in feature identification.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables cost-effective and environmentally friendly seismic data acquisition and processing, allowing for accurate identification of subterranean features such as hydrocarbon reservoirs.
Implementation Method 1
a high-speed train traversing a train track at a unique speed... seismic waves produced by a high-speed train
Data Source
AI summary
Systems and a method are disclosed. The method includes obtaining a plurality of raw seismic datasets for a subterranean region of interest, wherein each raw seismic dataset is generated by a high-speed train traversing a train track at a unique speed. The method further includes determining a plurality of processed seismic datasets by processing each of the plurality of raw seismic datasets and determining a final seismic dataset by combining the plurality of processed seismic datasets. The method still further includes identifying subterranean features within the subterranean region of interest using the final seismic dataset.